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We propose an atlas-based method to segment the intracochlear anatomy (ICA)in the post-implantation CT (Post-CT) images of cochlear implant (CI)recipients that preserves the point-to-point correspondence between the meshesin the atlas and the segmented volumes. To solve this problem, which ischallenging because of the strong artifacts produced by the implant, we use apair of co-trained deep networks that generate dense deformation fields (DDFs)in opposite directions. One network is tasked with registering an atlas imageto the Post-CT images and the other network is tasked with registering thePost-CT images to the atlas image. The networks are trained using lossfunctions based on voxel-wise labels, image content, fiducial registrationerror, and cycle-consistency constraint. The segmentation of the ICA in thePost-CT images is subsequently obtained by transferring the predefinedsegmentation meshes of the ICA in the atlas image to the Post-CT images usingthe corresponding DDFs generated by the trained registration networks. Ourmodel can learn the underlying geometric features of the ICA even though theyare obscured by the metal artifacts. We show that our end-to-end networkproduces results that are comparable to the current state of the art (SOTA)that relies on a two-steps approach that first uses conditional generativeadversarial networks to synthesize artifact-free images from the Post-CT imagesand then uses an active shape model-based method to segment the ICA in thesynthetic images. Our method requires a fraction of the time needed by theSOTA, which is important for end-user acceptance.

Atlas-Based Segmentation of Intracochlear Anatomy in Metal Artifact Affected CT Images of the Ear with Co-trained Deep Neural Networks